A Deep Ensemble Learning Network-based Approach to Detect Neurodegenerative Diseases

Neurodegenerative diseases, such as Alzheimer’s and Parkinson’s, pose significant challenges to global healthcare systems due to their increasing prevalence and the complexity of their diagnosis. In recent years, machine learning techniques have emerged as promising tools for early and accurate detection of these diseases, aiding in timely intervention and improved patient outcomes. This research use two type of dataset Alzheimer’s and Parkinson’s collected from Kaggle. This study presents a novel approach to detecting neurodegenerative diseases using a combination of established algorithms and a newly proposed machine learning algorithm, the Deep Ensemble Learning Network (DELN). DELN integrates deep learning principles with ensemble learning strategies, allowing for robust feature extraction and improved model generalization. Our approach utilizes a diverse set of features extracted from neuroimaging data, genetic information, and clinical assessments. Through extensive experiments on large-scale datasets, we demonstrate that DELN outperforms existing algorithms in terms of accuracy, sensitivity, and specificity. DELN method showcased exceptional performance, achieving an outstanding accuracy of 96.99% in AD and achieving accuracy of 98.91% in Parkinson’s disease dataset. As neurodegenerative diseases continue to impact millions of lives worldwide, the integration of advanced machine learning techniques, exemplified by DELN, offers a beacon of hope for early and precise diagnosis, paving the way for targeted therapeutic interventions and improved patient care.